Cold-start Sequential Recommendation via Meta Learner
Authors: Yujia Zheng, Siyi Liu, Zekun Li, Shu Wu4706-4713
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments conducted on three real-world datasets verify the superiority of Mecos, with the average improvement up to 99%, 91%, and 70% in HR@10 over state-of-the-art baseline methods. |
| Researcher Affiliation | Academia | Yujia Zheng,1 Siyi Liu, 1 Zekun Li, 2, 3 Shu Wu 4, 5, * 1 University of Electronic Science and Technology of China 2 School of Cyber Security, University of Chinese Academy of Sciences 3 Institute of Information Engineering, Chinese Academy of Sciences 4 School of Artificial Intelligence, University of Chinese Academy of Sciences 5 Institute of Automation and Artificial Intelligence Research, Chinese Academy of Sciences |
| Pseudocode | Yes | Algorithm 1: Meta-training process |
| Open Source Code | No | The paper does not explicitly state that source code for the proposed Mecos framework is made publicly available. |
| Open Datasets | Yes | We use three public benchmark datasets for experiments. The first one is based on Steam (Kang and Mc Auley 2018), which contains user s reviews of online games from Steam, a large online video game platform. The second one is based on Amazon Electronic , which is crawled from amazon.com by (Mc Auley et al. 2015). The last one is based on Tmall from IJCAI-15 competition. This dataset contains user behavior logs in Tmall, the largest e-commerce platform in China. We apply the same preprocessing as (Kang and Mc Auley 2018; Tang and Wang 2018). Same as (Liu et al. 2018; Tan, Xu, and Liu 2016), the data augmentation strategy is employed on all datasets (e.g., a sequence (v0, v1, v2, v3) is divided into three successive sequences: (v0, v1), (v0, v1, v2), (v0, v1, v2, v3)). And that strategy is proved to be effective by previous studies (Liu et al. 2018; Tan, Xu, and Liu 2016). |
| Dataset Splits | Yes | Besides, the proportion of training, validation and testing tasks is 7 : 1 : 2. Moreover, we randomly leave out a subset of labels in Ytrain to generate the validation set Tmeta-valid. |
| Hardware Specification | No | The paper does not specify the hardware (e.g., CPU, GPU models, memory, or cluster configurations) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies (e.g., library names with version numbers) used for the implementation. |
| Experiment Setup | Yes | Each ground-truth next-click item is paired with 127 negative items (N = 128) randomly sampled from Ytest. All models are trained with the same data (pre-train data, all sequences in Tmeta-train and support sets in Tmeta-valid/Tmeta-test) for fair comparisons. Moreover, we vary the K to investigate the framework performance with different support set sizes and report K = 3 in default. Besides, the matching step t and hidden dimensionality d is set to 2 and 100, respectively. All hyper-parameters are chosen based on model s performances on Tmeta-valid. We run all models five times with different random seeds and report the average. |